| cr.plots {car} | R Documentation |
These functions construct component+residual plots (also called partial-residual plots) for linear and generalized linear models.
cr.plots(model, variable, ask=missing(variable), one.page=!ask,
span=0.5, ...)
crp(...)
cr.plot(model, ...)
## S3 method for class 'lm':
cr.plot(model, variable, order=1, line=TRUE, smooth=TRUE, iter,
span=0.5, las=par('las'), col=palette()[2], pch=1, lwd=2,
main="Component+Residual Plot", ...)
## S3 method for class 'glm':
cr.plot(model, ...)
model |
model object produced by lm or glm. |
variable |
variable (if it exists in the search path) or
name of variable. This argument usually is omitted for crp
or cr.plots. |
ask |
if TRUE, a menu is provided in the R Console for the
user to select the variable(s) to plot, and to modify the span for the smoother
used to draw a nonparametric-regression line on the plot. |
one.page |
if TRUE (and ask=FALSE), put all plots on one
graph. |
order |
order of polynomial regression performed for predictor to be plotted. |
line |
TRUE to plot least-squares line. |
smooth |
TRUE to plot nonparametric-regression (lowess) line. |
iter |
number of robustness iterations for nonparametric-regression smooth; defaults to 3 for a linear model and to 0 for a non-Gaussian glm. |
span |
span for lowess smoother. |
las |
if 0, ticks labels are drawn parallel to the
axis; set to 1 for horizontal labels (see par). |
col |
color for points and lines; the default is the second entry
in the current color palette (see palette
and par). |
pch |
plotting character for points; default is 1
(a circle, see par). |
lwd |
line width; default is 2 (see par). |
main |
title for plot. |
... |
pass arguments down. |
The function intended for direct use is cr.plots (for which crp
is an abbreviation). By default, these functions are used interactively
through a text menu.
The model cannot contain interactions, but can contain factors. Parallel boxplots of the partial residuals are drawn for the levels of a factor.
NULL. These functions are used for their side effect: producing
plots.
John Fox jfox@mcmaster.ca
Cook, R. D. and Weisberg, S. (1999) Applied Regression, Including Computing and Graphics. Wiley.
Fox, J. (1997) Applied Regression, Linear Models, and Related Methods. Sage.
cr.plots(lm(prestige~income+education, data=Prestige), variable="income") ## Not run: cr.plots(glm(partic != "not.work" ~ hincome + children, data=Womenlf, family=binomial)) ## End(Not run)